DEA scores’ confidence intervals with past-present and past-present-future based resampling

Jamal Ouenniche, Kaoru Tone

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

In data envelopment analysis (DEA), input and output values are subject to change for several reasons. Such variations differ in their input/output items and their decision-making units (DMUs). Hence, DEA efficiency scores need to be examined by considering these factors. In this paper, we propose new resampling models based on these variations for gauging the confidence intervals of DEA scores. The first model utilizes past-present data for estimating data variations imposing chronological order weights which are supplied by Lucas series (a variant of Fibonacci series). The second model deals with future prospects. This model aims at forecasting the future efficiency score and its confidence interval for each DMU. We applied our models to a dataset composed of Japanese municipal hospitals.
Original languageEnglish
Pages (from-to)121-135
Number of pages16
JournalAmerican Journal of Operations Research
Publication statusPublished - 8 Mar 2016

Keywords / Materials (for Non-textual outputs)

  • Data Variation
  • Resampling
  • Confidence Interval
  • Past - Present - Future DEA
  • Hospital


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